def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=0.05, random_state=0, penalty="l1/l2",
loss="squared", max_iter=100)
lb = LabelBinarizer()
Y = lb.fit_transform(mult_target)
reg.fit(mult_dense, Y)
y_pred = lb.inverse_transform(reg.predict(mult_dense))
assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)
def test_fit_reg_squared_multiple_outputs():
reg = CDRegressor(C=0.05,
random_state=0,
penalty="l1/l2",
loss="squared",
max_iter=100)
lb = LabelBinarizer()
Y = lb.fit_transform(mult_target)
reg.fit(mult_dense, Y)
y_pred = lb.inverse_transform(reg.predict(mult_dense))
assert_almost_equal(np.mean(y_pred == mult_target), 0.797, 3)
assert_almost_equal(reg.n_nonzero(percentage=True), 0.5)
